Snakemake Report

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Workflow

NanoClass

Click the nodes to obtain details about each step.

Classification

Read-processing

Runtime

Statistics

If the workflow has been executed in cluster/cloud, runtimes include the waiting time in the queue.

Jobs 21

Input files

Output files

data/{run,[A-Za-z0-9]+}/porechopped/{sample,[A-Za-z0-9]+}.trimmed.fastq.gz

Conda software stack

porechop=0.2.4
chopper=0.6.0
nanostat=1.4.0
seqtk=1.3
coreutils
sed
pip
python=3.10.13
{'pip': ['pistis==0.3.3', 'numpy===1.26.2', 'matplotlib==3.8.2', 'seaborn==0.13.0']}

Code

1 2	`porechop --input {input} --output {output} --threads {threads} --check_reads {params.check_reads} --discard_middle > {log}`

Jobs 21

Input files

data/{run}/chopper/{sample}.filtered.fastq.gz

Output files

plots/{run,[A-Za-z0-9]+}/chopper/{sample,[A-Za-z0-9]+}.filtered.pdf

Conda software stack

porechop=0.2.4
chopper=0.6.0
nanostat=1.4.0
seqtk=1.3
coreutils
sed
pip
python=3.10.13
{'pip': ['pistis==0.3.3', 'numpy===1.26.2', 'matplotlib==3.8.2', 'seaborn==0.13.0']}

Code

1 2	`pistis --fastq {input} --output {output} --downsample {params.downsample} 2> {log}`

Jobs 21

Input files

data/{run}/porechopped/{sample}.trimmed.fastq.gz

Output files

data/{run,[A-Za-z0-9]+}/chopper/{sample,[A-Za-z0-9]+}.filtered.fastq.gz

Conda software stack

porechop=0.2.4
chopper=0.6.0
nanostat=1.4.0
seqtk=1.3
coreutils
sed
pip
python=3.10.13
{'pip': ['pistis==0.3.3', 'numpy===1.26.2', 'matplotlib==3.8.2', 'seaborn==0.13.0']}

Code

1
2

        gunzip -c {input} |            chopper -q {params.quality}             --headcrop {params.headcrop} --tailcrop {params.tailcrop}               -l {params.min_len} --maxlength {params.max_len}  --threads {params.threads} |            gzip > {output}  2> {log}

Jobs 21

Input files

data/{run}/chopper/{sample}.filtered.fastq.gz

Output files

stats/{run,[A-Za-z0-9]+}/chopper/{sample,[A-Za-z0-9]+}.filtered.txt

Conda software stack

porechop=0.2.4
chopper=0.6.0
nanostat=1.4.0
seqtk=1.3
coreutils
sed
pip
python=3.10.13
{'pip': ['pistis==0.3.3', 'numpy===1.26.2', 'matplotlib==3.8.2', 'seaborn==0.13.0']}

Code

1 2	`NanoStat --fastq {input} --name {output} --threads {threads} 2> {log}`

Jobs 21

Input files

classifications/{run}/minimap/{sample}.minimap.taxlist

Output files

classifications/{run,[A-Za-z0-9]+}/minimap/{sample,[A-Za-z0-9]+}.minimap.taxmat
classifications/{run,[A-Za-z0-9]+}/minimap/{sample,[A-Za-z0-9]+}.minimap.otumat

Conda software stack

minimap2=2.17
samtools=1.10
python=3.8.5

Code

1	`python3 {SRCDIR}/tomat.py -l {input} 2> {log}`

Jobs 21

Input files

classifications/{run}/minimap/{sample}.minimap.out
/zfs/omics/projects/amplicomics/bin/NanoClass2/./db/common/ref-taxonomy.txt

Output files

classifications/{run,[A-Za-z0-9]+}/minimap/{sample,[A-Za-z0-9]+}.minimap.taxlist

Conda software stack

coreutils
grep
gawk
python=3.8.5
blast=2.10.1
fasta-splitter=0.2.6

Code

1 2	`python3 {SRCDIR}/tolca.py -b {input.mm} -t {input.db} -l {output} -c {params.lcacons} > {log}`

Jobs 21

Input files

classifications/{run}/minimap/{sample}.minimap.paf

Output files

classifications/{run,[A-Za-z0-9]+}/minimap/{sample,[A-Za-z0-9]+}.minimap.out

Conda software stack

minimap2=2.17
samtools=1.10
python=3.8.5

Code

1
2

    sed 's/AS:i://' {input} |         awk -F'	' -v OFS='	' '{{len=$2; start=$3; end=$4; print $1, $6, $12, $15, len, start, end, $10, $11, (end-start+1)/len*100, $10/$11}}' |         python3 {SRCDIR}/filter_paf.py -i - -o - -c {params.coverage_threshold} |         awk -v OFS="	" '{{print $1, $2}}' >  {output}

Jobs 21

Input files

/zfs/omics/projects/amplicomics/bin/NanoClass2/./db/common/ref-seqs.fna

Output files

classifications/{run,[A-Za-z0-9]+}/minimap/{sample,[A-Za-z0-9]+}.minimap.paf

Conda software stack

minimap2=2.17
samtools=1.10
python=3.8.5

Code

1 2	`minimap2 {params.extra} -t {threads} -N {params.nseqs} {input.target} {input.query} -o {output} 2> {log}`

Jobs 1

Input files

classifications/mic2024/minimap/BC01.minimap.taxmat
classifications/mic2024/minimap/BC02.minimap.taxmat
classifications/mic2024/minimap/BC03.minimap.taxmat
classifications/mic2024/minimap/BC05.minimap.taxmat
classifications/mic2024/minimap/BC06.minimap.taxmat
classifications/mic2024/minimap/BC07.minimap.taxmat
classifications/mic2024/minimap/BC09.minimap.taxmat
classifications/mic2024/minimap/BC10.minimap.taxmat
classifications/mic2024/minimap/BC11.minimap.taxmat
classifications/mic2024/minimap/BC12.minimap.taxmat
classifications/mic2024/minimap/BC13.minimap.taxmat
classifications/mic2024/minimap/BC14.minimap.taxmat
classifications/mic2024/minimap/BC15.minimap.taxmat
classifications/mic2024/minimap/BC16.minimap.taxmat
classifications/mic2024/minimap/BC17.minimap.taxmat
classifications/mic2024/minimap/BC18.minimap.taxmat
classifications/mic2024/minimap/BC19.minimap.taxmat
classifications/mic2024/minimap/BC20.minimap.taxmat
classifications/mic2024/minimap/BC22.minimap.taxmat
classifications/mic2024/minimap/BC23.minimap.taxmat
classifications/mic2024/minimap/BC24.minimap.taxmat

Output files

plots/aabund-Domain-by-sample.pdf
plots/rabund-Domain-by-sample.pdf
plots/aabund-Phylum-by-sample.pdf
plots/rabund-Phylum-by-sample.pdf
plots/aabund-Class-by-sample.pdf
plots/rabund-Class-by-sample.pdf
plots/aabund-Order-by-sample.pdf
plots/rabund-Order-by-sample.pdf
plots/aabund-Family-by-sample.pdf
plots/rabund-Family-by-sample.pdf
plots/aabund-Genus-by-sample.pdf
plots/rabund-Genus-by-sample.pdf

Conda software stack

python=3.9.7
taxonkit=0.15.0
r-essentials=1.7.0
r-base=4.0.5
r-rlang=1.0.5
r=4.0
bioconductor-phyloseq=1.34.0
bioconductor-dada2=1.18.0
r-seqinr=4.2_16
bioconductor-decipher=2.18.1
r-vroom=1.5.7
pip
{'pip': ['biopython==1.78']}

Code

1
2
3

        mkdir -p ./plots
        Rscript {SRCDIR}/barplot.R {input} 2> {log}

Jobs 1

Input files

benchmarks/mic2024/minimap_classify_BC01.txt
benchmarks/mic2024/minimap_classify_BC02.txt
benchmarks/mic2024/minimap_classify_BC03.txt
benchmarks/mic2024/minimap_classify_BC05.txt
benchmarks/mic2024/minimap_classify_BC06.txt
benchmarks/mic2024/minimap_classify_BC07.txt
benchmarks/mic2024/minimap_classify_BC09.txt
benchmarks/mic2024/minimap_classify_BC10.txt
benchmarks/mic2024/minimap_classify_BC11.txt
benchmarks/mic2024/minimap_classify_BC12.txt
benchmarks/mic2024/minimap_classify_BC13.txt
benchmarks/mic2024/minimap_classify_BC14.txt
benchmarks/mic2024/minimap_classify_BC15.txt
benchmarks/mic2024/minimap_classify_BC16.txt
benchmarks/mic2024/minimap_classify_BC17.txt
benchmarks/mic2024/minimap_classify_BC18.txt
benchmarks/mic2024/minimap_classify_BC19.txt
benchmarks/mic2024/minimap_classify_BC20.txt
benchmarks/mic2024/minimap_classify_BC22.txt
benchmarks/mic2024/minimap_classify_BC23.txt
benchmarks/mic2024/minimap_classify_BC24.txt

Output files

plots/runtime-by-sample.pdf
plots/runtime_log-by-sample.pdf

Conda software stack

python=3.9.7
taxonkit=0.15.0
r-essentials=1.7.0
r-base=4.0.5
r-rlang=1.0.5
r=4.0
bioconductor-phyloseq=1.34.0
bioconductor-dada2=1.18.0
r-seqinr=4.2_16
bioconductor-decipher=2.18.1
r-vroom=1.5.7
pip
{'pip': ['biopython==1.78']}

Code

        mkdir -p ./plots
        mkdir -p ./tables
        Rscript {SRCDIR}/timeplot.R {input} 2> {log}